code: def _features():
      return {
          "query_length": tf.convert_to_tensor(value=[[1], [2]]),
          "utility": tf.convert_to_tensor(
              value=[[[1.0], [0.0]], [[0.0], [1.0]]]
          ),
          "unigrams": tf.SparseTensor(
              indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0]],
              values=[
                  "ranking",
                  "regression",
                  "classification",
                  "ordinal",
              ],
              dense_shape=[2, 2, 1],
          ),
          "example_feature_size": tf.convert_to_tensor(value=[1, 2]),
      }
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import six
  import tensorflow.compat.v2
  from tensorflow_ranking.python.keras import feature
label_desc: Use image node_id 2 for calling a global function with example usage: _features() and returns: dict